You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. If your research question does not include a categorical variable, you can categorize one that is quantitative. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test.
#Statistical tools for data analysis how to
Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically.
#Statistical tools for data analysis software
The software includes a customizable interface, and even though it may be hard form someone to use, it is relatively easy for those experienced in how it works.This session starts where the Data Management and Visualization course left off. From data preparation and data management to analysis and reporting. For example, IBM SPSS Statistics covers much of the analytical process. IBM SPSS Statistics, RMP and Stata are some examples of statistical analysis software. Software for statistical analysis will typically allow users to do more complex analyses by including additional tools for organization and interpretation of data sets, as well as for the presentation of that data.